To get IEEE 2015-2017 Project for above title in .Net or Java
mail to finalyearprojects2all@gmail.com or contact +91 8870791415
IEEE 2015-2016 Project Videos: https://www.youtube.com/channel/UCyK6peTIU3wPIJxXD0MbNvA
Measures of Central Tendency: Mean, Median and Mode
Service usage classification with encrypted internet traffic in mobile messaging apps
1. IEEE PROJECTS DEVELOPMENTS
WE OFFER IEEE PROJECTS MCA FINAL YEAR STUDENT PROJECTS, ENGINEERING
PROJECTS AND TRAINING, PHP PROJECTS, JAVA AND J2EE PROJECTS, ASP.NET
PROJECTS, NS2 PROJECTS, MATLAB PROJECTS AND IPT TRAINING .
CELL: +91 8870791415
Mail to: finalyearprojects2all@gmail.com
Service Usage Classification with Encrypted Internet Traffic in Mobile Messaging Apps
Abstract
The rapid adoption of mobile messaging Apps has enabled us to collect massive amount
of encrypted Internet traffic of mobile messaging. The classification of this traffic into different
types of in-App service usages can help for intelligent network management, such as managing
network bandwidth budget and providing quality of services. Traditional approaches for
classification of Internet traffic rely on packet inspection, such as parsing HTTP headers.
However, messaging Apps are increasingly using secure protocols, such as HTTPS and SSL, to
transmit data. This imposes significant challenges on the performances of service usage
classification by packet inspection. To this end, in this paper, we investigate how to exploit
encrypted Internet traffic for classifying in-App usages. Specifically, we develop a system,
named CUMMA, for classifying service usages of mobile messaging Apps by jointly modeling
user behavioral patterns, network traffic characteristics and temporal dependencies. Along this
line, we first segment Internet traffic from traffic-flows into sessions with a number of dialogs in
a hierarchical way. Also, we extract the discriminative features of traffic data from two
perspectives: (i) packet length and (ii) time delay. Next, we learn a service usage predictor to
classify these segmented dialogs into single-type usages or outliers. In addition, we design a
clustering Hidden Markov Model (HMM) based method to detect mixed dialogs from outliers
and decompose mixed dialogs into sub-dialogs of single-type usage. Indeed, CUMMA enables
mobile analysts to identify service usages and analyze end-user in-App behaviors even for
encrypted Internet traffic. Finally, the extensive experiments on real-world messaging data
demonstrate the effectiveness and efficiency of the proposed method for service usage
classification.
2. IEEE PROJECTS DEVELOPMENTS
WE OFFER IEEE PROJECTS MCA FINAL YEAR STUDENT PROJECTS, ENGINEERING
PROJECTS AND TRAINING, PHP PROJECTS, JAVA AND J2EE PROJECTS, ASP.NET
PROJECTS, NS2 PROJECTS, MATLAB PROJECTS AND IPT TRAINING .
CELL: +91 8870791415
Mail to: finalyearprojects2all@gmail.com
System Specification
System Requirements:
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
3. IEEE PROJECTS DEVELOPMENTS
WE OFFER IEEE PROJECTS MCA FINAL YEAR STUDENT PROJECTS, ENGINEERING
PROJECTS AND TRAINING, PHP PROJECTS, JAVA AND J2EE PROJECTS, ASP.NET
PROJECTS, NS2 PROJECTS, MATLAB PROJECTS AND IPT TRAINING .
CELL: +91 8870791415
Mail to: finalyearprojects2all@gmail.com
• Mouse : Logitech.
• Ram : 512 Mb.
Software Requirements:
• Operating system : - Windows 7. 32 bit
• Coding Language : C#.net 4.0
• Data Base : SQL Server 2008